III: Small: Methods for Auditing and Enhancing Completeness of Ontologies

III：小：审计和增强本体完整性的方法

• 批准号: 1931134
• 负责人: Licong Cui
• 金额: $ 30.69万
• 依托单位: The University of Texas Health Science Center at Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931134&HistoricalAwards=false
• 关键词: III; Small; Methods; Auditing; Enhancing

An ontology is a formal representation of the knowledge by a set of concepts (terms) and the relationships between those concepts within a domain of specialty. Ontologies have been widely used for orchestrating the coding, management, exchange, and sharing of the increasingly large amounts of digital data produced by the biomedical research enterprise. For example, SNOMED CT, one of the largest and most complex biomedical ontologies, supports the development of high-quality electronic health records and facilitates patient subgroup identification, clinical decision support, and healthcare delivery quality measurement. Given such important roles that biomedical ontologies play, quality issues (such as incompleteness in coverage of subclasses), if not addressed, can affect the quality of diagnoses and decisions. However, incompleteness issues such as missing hierarchical relations and missing concepts are infeasible to be addressed by manual work alone due to the size and complexity of biomedical ontologies. The goal of this project is to develop automated and scalable approaches for identifying potential incompleteness issues as well as suggesting solutions to fix them. This project will incorporate the computational aspects of the proposed work into curriculum development and educational offerings related to data science and promote women participation in data science.To audit and enhance completeness of ontologies, this project explores the following research tasks: (1) Development of a robust reasoning framework for detecting and repairing missing subclass or hierarchical relations. This will result in suggestions that directly enhances the subclass completeness of ontologies; (2) Development of novel methods for identifying missing concepts and creating appropriate name labels for the identified missing concepts. This will result in enhancement in the concept completeness of ontologies; (3) Generation of supporting evidence for suggested solutions by leveraging rich extrinsic knowledge. For further verification of the robustness of the proposed approaches, domain experts will be involved in validation of the discovered incompleteness issues. The proposed approaches are applied to three large ontologies in biomedicine: SNOMED CT, Gene Ontology, and NCI Thesaurus. Suggested ontology changes will be communicated to the respective ontology owners for incorporation in subsequent versions. The project website will include further information about this project, and provide access to publications, software, datasets and curriculum material.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本体是通过一组概念（术语）和专业领域内这些概念之间的关系对知识的正式表示。本体已被广泛用于编排编码、管理、交换和共享生物医学研究企业产生的越来越多的数字数据。例如，SNOMED CT是最大和最复杂的生物医学本体之一，它支持开发高质量的电子健康记录，并促进患者亚组识别、临床决策支持和医疗保健交付质量测量。鉴于生物医学本体扮演着如此重要的角色，如果不解决质量问题（如子类覆盖的不完整），可能会影响诊断和决策的质量。然而，由于生物医学本体的规模和复杂性，缺少层次关系和概念等不完备性问题无法通过单独的手工工作来解决。这个项目的目标是开发自动化和可伸缩的方法来识别潜在的不完整性问题，并建议解决方案来修复它们。该项目将把拟议工作的计算方面纳入与数据科学有关的课程制定和教育产品，并促进妇女参与数据科学。为了审计和增强本体的完整性，本项目探讨了以下研究任务：(1)开发一个健壮的推理框架，用于检测和修复缺失的子类或层次关系。这将产生直接增强本体子类完整性的建议；(2)开发识别缺失概念的新方法，并为识别的缺失概念创建适当的名称标签。这将增强本体的概念完整性；(3)利用丰富的外部知识为建议的解决方案生成支持性证据。为了进一步验证所提出方法的鲁棒性，领域专家将参与对所发现的不完整性问题的验证。提出的方法应用于生物医学中的三个大型本体：SNOMED CT、基因本体和NCI词库。建议的本体更改将传达给各自的本体所有者，以便在后续版本中合并。该项目的网站将包括有关该项目的进一步信息，并提供访问出版物、软件、数据集和课程材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Enhancing the Quality of Hierarchic Relations in the National Cancer Institute Thesaurus to Enable Faceted Query of Cancer Registry Data

• DOI: 10.1200/cci.19.00124
• 发表时间: 2020-05-06
• 期刊: JCO CLINICAL CANCER INFORMATICS
• 影响因子: 4.2
• 作者: Cui, Licong;Abeysinghe, Rashmie;Zhang, Guo-Qiang
• 通讯作者: Zhang, Guo-Qiang

Leveraging Non-lattice Subgraphs to Audit Hierarchical Relations in NCI Thesaurus

利用非格子图审计 NCI 同义词库中的层次关系

• 发表时间: 2019
• 期刊: AMIA Annual Symposium proceedings
• 作者: Abeysinghe, Rashmie;Brooks, Michael A;Cui, Licong
• 通讯作者: Cui, Licong

A lexical-based approach for exhaustive detection of missing hierarchical IS-A relations in SNOMED CT

一种基于词汇的方法，用于详尽检测 SNOMED CT 中缺失的分层 IS-A 关系

• 发表时间: 2020
• 期刊: AMIA Annual Symposium proceedings
• 作者: Zheng, Fengbo;Shi, Jay;Cui, Licong
• 通讯作者: Cui, Licong

Web-based Interactive Visualization of Non-Lattice Subgraphs (WINS) in SNOMED CT.

SNOMED CT 中基于 Web 的非格子图 (WINS) 交互式可视化。

• 发表时间: 2020
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Zhu,Wei;Tao,Shiqiang;Cui,Licong;Zhang,Guo-Qiang
• 通讯作者: Zhang,Guo-Qiang

A Comparison of Exhaustive and Non-lattice-based Methods for Auditing Hierarchical Relations in Gene Ontology

基因本体中层次关系审计的穷举方法和非基于格的方法的比较

• 发表时间: 2021
• 期刊: AMIA 2021 Annual Symposium
• 作者: Abeysinghe, Rashmie;Zheng, Fengbo;Cui, Licong
• 通讯作者: Cui, Licong